Google Has the All-Important AI Edge Over Microsoft

December 29, 2018 By News Team By News Team

Microsoft (MSFT) and Alphabet (GOOGL) are eerily similar equities, having slumped exactly the same amount this year, having similar growth profiles, and identical market capitalizations and valuations.

Which do you choose?

Both are excellent companies, and a credible case can be made for either. When one strips away the differences in their lines of revenue — operating system software versus advertising, for example — it comes down to dominance in the software ecosystem. And in that regard, both Microsoft and Alphabet have powerful cloud computing operations that provide each with leadership in the most important category of computing software today, machine learning.

But Alphabet’s Google unit arguably has the upper hand on Microsoft in a couple of ways.

At $751 billion in market capitalization, Microsoft topped Google’s market cap, $707 billion, back in September of this year, holding onto a 13% gain for the year, while Alphabet is down about 4%. The forward price-to-earnings multiple for the two is similar, with Microsoft being slightly cheaper, at 19 times versus Alphabet at 22 times. Both are rather expensive relative to the overall market, obviously. Google has the edge on revenue growth, with 19% projected for 2019, versus 11% for Microsoft’s fiscal 2020 revenue growth.

The difference between the two comes down to what’s going on in those cloud operations. Microsoft has scale, but it may not have the more important relative position with developers.

Machine learning, whereby computers can develop some of the code they use automatically by extracting patterns from data, is the most important trend in programming today.

Both companies, along with Amazon (AMZN) , have deployed machine learning tools in their cloud computing operations. Microsoft’s Azure cloud service is generally viewed as second only to Amazon Web Services in terms of revenue, with Google Cloud a distant third. But since neither Microsoft nor Google disclose revenue for the units, the comparison is sketchy.

Nevertheless, it’s safe to say their cloud operations are important assets through which both companies attract and retain developers of machine learning neural networks.

Google, however, has control of the most important “framework” for building machine learning applications, called TensorFlow. A framework is a collection of pre-built code that can be employed by a software developer to piece together programs without reinventing the wheel. Right now, TensorFlow is the dominant framework for machine learning.

It’s not the only one, and in fact, Microsoft has some important offerings of its own. They include something called the “infer.NET” framework, and another called the “Microsoft Cognitive Toolkit.” Both are being used by developers in a number of interesting projects.

But again and again, the vast bulk of work one comes across in machine learning is via TensorFlow; either that, or another couple of frameworks by companies other than Microsoft, such as “PyTorch” from Facebook; a framework called “Caffe” originally developed at the University of California at Berkley; and newer initiatives that build on top of those programs, such as “fast.ai,” by the San Francisco startup of that name.

Nothing about Microsoft’s $7.5 billion purchase of the developer community GitHub, in October, changes any of that. Machine learning frameworks win on the strengths or weaknesses they possess, as well as how widely supported they are. Owning the most popular programming repository, GitHub, doesn’t change the decisions about frameworks that individual developers make.

Why the control of frameworks matter is that Google has been building on the use of TensorFlow. They have their own custom chips running in their cloud operations, called “Tensor Processing Units,” or TPUs, which have become a leading-edge piece of silicon and are made available by Google in its cloud computing service. Combined with TensorFlow software, TPUs give Google a chip-and-software combo that is an edge over what Microsoft currently offers.

And TensorFlow has spread rapidly to mobile computing operations, where Google has the dominant worldwide presence, through control of the Android operating system, and Microsoft has no presence. A study put out in November, by researchers that included Peking University but also Microsoft’s own research unit in Beijing, found that among top mobile applications for Android, of those that use machine learning in some form, fully half use either TensorFlow; “TFLite,” a smaller subset of TensorFlow created by Google just for mobile devices; or “ncnn,” another framework created by Chinese social networking service Tencent (TCEHY) . Microsoft’s code is not even in the running as far as machine learning on mobile devices.

This entire field of machine learning is still very young, and very new. There is much innovation to come, and Google’s relative lead to shrink and evaporate over time. An interesting initiative by Microsoft is its championing of something called “Open Neural Network Exchange,” or ONNX, which may make developers less dependent on individual frameworks over time.

Time will tell: in the meantime, however, Alphabet’s Google has the edge on Microsoft in the core tools used for the most cutting-edge types of application development. That’s worth a bid.